Inspired by the success of PCQA methods via utilizing the image modality [1]}, [2]}, we propose to use a 2D image encoder for feature extraction. \(N_{I}\)  image projections are rendered from the distorted colored point clouds from random viewpoints but with a fixed viewing distance to keep the texture consistent. Considering that the perceived texture visual quality varies at different scales [3]}, [4]}, we propose to use hierarchical 2D CNN backbones to make use of multi-scale quality-aware features to better model the human perception of texture quality. Assume that the 2D CNN backbone has \(j\)  layers, the hierarchical features can be described as:
\(\begin{aligned}\theta _{I}(x) &= \alpha _{1}(x)\oplus \alpha _{2}(x) \cdots \oplus \alpha _{j}(x),\\\alpha _{k}(x) &= {\rm {GAP}}(L_{k}(x)), k \in \lbrace 1,\cdots , j\rbrace ,\end{aligned}\) 
